Lead Software Engineer - Java , Kubernetics, Infrastructure

JOB DESCRIPTION

We have an opportunity to impact your career and provide an adventure where you can push the limits of what’s possible.

As a Lead Software Engineer at JPMorganChase, within Delivery Platforms you will be a technical leader and hands-on builder of the firm’s composable test environment platform — enabling developers across a 10,000-engineer organization to spin up isolated, production-representative virtual environments per pull request, run integration tests independently, and merge without contention on shared environments. You will operate across hybrid infrastructure (on-premises Kubernetes and AWS), solve complex problems in traffic routing, dependency graph resolution, and stateful service provisioning, and deliver a platform that thousands of engineers rely on daily.

Job Responsibilities

• Designs and implement core subsystems of the composable environment platform — including the provisioning control plane, lifecycle management engine, resource scheduling, and automatic teardown with time-to-live enforcement
• Builds and own the traffic routing and isolation layer that directs requests to the correct service versions within virtual environment sessions using header-based routing, service mesh integration, and service discovery — ensuring zero cross-session contamination.
• Implements dependency graph resolution logic that determines which services are instantiated live, reused from baseline, or virtualized/stubbed for a given test scenario — enabling composable environments at 1,000+ service scale
• Develops and maintain the self-service interfaces (CLI and API) that enable application teams to declare environment needs, provision in minutes, execute tests, and tear down — with clear contracts, versioning, and backward compatibility
• Builds automated provisioning for stateful test infrastructure across heterogeneous data stores (Oracle, Kafka, Cassandra, MongoDB, CockroachDB, DynamoDB, PostgreSQL), including snapshot/restore, schema versioning, and synthetic data seeding
• Implements CI/CD pipeline integrations — environments created on PR open, tests executed, results reported, environments destroyed on merge/close — across Jenkins, Spinnaker, GitHub Actions, and Argo CD
• Builds observability, operational dashboards, and alerting for the platform itself — tracking time-to-environment, provisioning success rates, resource utilization, cost attribution, and isolation correctness
• Ensures the platform operates reliably across hybrid infrastructure (on-premises Kubernetes and EKS) with consistent provisioning semantics, cross-network connectivity, and acceptable spin-up latency regardless of hosting tier. Mentors and provide technical guidance to staff engineers on the team, conduct design reviews, and establish coding standards and testing practices for platform code
• Architects and governs agentic AI-enabled engineering workflows (using enterprise-authorized tools within the work environment) to improve delivery speed, code quality, and operational outcomes at scale (e.g., AI-driven PR review assistance, test generation/maintenance, release readiness checks, incident triage and root-cause acceleration), while defining guardrails for validation, security, resiliency, and reuse across teams
• Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.

Required Qualifications, Capabilities, and Skills

• 12+ years of professional software engineering experience, with significant hands-on experience building internal platforms, infrastructure automation, or developer tooling consumed by large engineering organizations
• Strong hands-on proficiency in Go, Rust, or Java for building production-grade platform services — including API design, concurrency, fault tolerance, and operational reliability at scale
• Deep experience with Kubernetes (on-premises and EKS) — including namespace ma…